BagOfWords.py 文件源码

def local_bow_train(image):
        instances = []
        img_descriptors = []
        master_descriptors = []
        cv2.ocl.setUseOpenCL(False)
        orb = cv2.ORB_create()
        for img, label in images:
            print img
            img = read_color_image(img)
            keypoints = orb.detect(img, None)
            keypoints, descriptors = orb.compute(img, keypoints)
            if descriptors is None:
                descriptors = []

            img_descriptors.append(descriptors)
            for i in descriptors:
                master_descriptors.append(i)


        master_descriptors = np.float32(master_descriptors)
        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)

        ret, labels, centers = cv2.kmeans(master_descriptors, self.center_num, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
        labels = labels.ravel()

        count = 0
        img_num = 0
        for img, label in images:
            histogram = np.zeros(self.center_num)
            feature_vector = img_descriptors[img_num]
            for f in xrange(len(feature_vector)):
                index = count + f
                histogram.itemset(labels[index], 1 + histogram.item(labels[index]))
            count += len(feature_vector)
            pairing = Instance(histogram, label)
            instances.append(pairing)

        self.training_instances = instances
        self.centers = centers